JP2014063621A - Diagnostic apparatus and method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To determine the residual value of a storage battery easily and accurately.SOLUTION: A determination apparatus includes storage means for storing the test data, obtained from a plurality of test samples, about the deterioration characteristics of internal state amount of a storage battery for the number of charge/discharge cycles, for each different test conditions, first calculation means for identifying equivalent history corresponding to the history, when charge/discharge is actually performed by a storage battery with reference to the test data, based on the internal state amount of a storage battery detected when a diagnosed storage battery was charged/discharged, and calculating the likelihood of equivalent history identified from the data of a plurality of test samples included in the test data, and output means for outputting the likelihood thus calculated.

Description

  Embodiments described herein relate generally to a diagnostic apparatus and method.

  Conventionally, there is a method for estimating an internal state quantity using a discharge curve of a secondary battery (storage battery). Based on the internal state quantity estimated by this method, the deterioration state of the storage battery is estimated, and the remaining value of the storage battery is determined by evaluating the margin with respect to the use limit.

Japanese Patent No. 3669673

  However, in the above-described prior art, an internal state quantity that is greatly different from an actual value may be estimated due to an error, and the residual value may not be correctly determined. Further, in order to reduce the influence of errors, it is necessary to collect storage battery usage histories sequentially, and it is difficult to easily and accurately determine the residual value of the storage battery.

  In order to solve the above-described problem, the diagnostic apparatus according to the embodiment stores, for each different test condition, test data obtained from a plurality of test samples, which are deterioration characteristics of the internal state quantity of the storage battery with respect to the number of charge / discharge cycles. And an equivalent equivalent to a history of actually charging / discharging the storage battery with reference to the test data, based on the internal state quantity of the storage battery detected when charging / discharging the storage battery to be diagnosed A first calculating means for identifying a history and calculating a likelihood of the identified equivalent history from data for a plurality of test samples included in the test data; the identified equivalent history; and the calculated Output means for outputting the likelihood.

  In addition, the method of the embodiment is a method of a diagnostic apparatus provided with a storage unit that stores test data obtained from a plurality of test samples, with respect to the number of charge / discharge cycles, for each different test condition. It corresponds to the history of actually charging / discharging the storage battery with reference to the test data based on the internal state quantity of the storage battery detected when charging / discharging the storage battery to be diagnosed. Identifying an equivalent history, calculating a likelihood of the identified equivalent history from data for a plurality of test samples included in the test data, the identified equivalent history, and the calculated likelihood Output.

FIG. 1 is a block diagram illustrating a hardware configuration of the diagnostic apparatus according to the first embodiment. FIG. 2 is a block diagram illustrating a functional configuration of the diagnostic apparatus according to the first embodiment. FIG. 3 is a flowchart illustrating an example of the residual value diagnosis process. FIG. 4 is a graph showing an example of a discharge curve. FIG. 5 is a diagram illustrating an example of parameters and physical constants. FIG. 6 is a graph showing an example of a charging curve. FIG. 7 is a diagram illustrating an example of test data. FIG. 8 is a diagram illustrating identification of equivalent history. FIG. 9 is a diagram illustrating the calculation of the identification likelihood of the equivalent history. FIG. 10 is a flowchart illustrating an example of identification of the number of equivalent cycles and calculation of identification likelihood. FIG. 11 is a conceptual diagram illustrating calculation and display of the existence probability distribution of the capacity of the storage battery. FIG. 12 is a conceptual diagram illustrating the calculation and display of the existence probability distribution of the resistance value of the storage battery. FIG. 13 is a conceptual diagram illustrating output of a residual value diagnosis result. FIG. 14 is a conceptual diagram illustrating output of a residual value diagnosis result. FIG. 15 is a conceptual diagram illustrating output of a residual value diagnosis result. FIG. 16 is a conceptual diagram illustrating output of a residual value diagnosis result. FIG. 17 is a conceptual diagram exemplifying display of a storage battery capacity and resistance value existence probability distribution. FIG. 18 is a block diagram illustrating a functional configuration of the diagnostic apparatus according to the second embodiment. FIG. 19 is a flowchart illustrating an example of the residual value diagnosis process. FIG. 20 is a conceptual diagram illustrating the reuse of diagnosis results. FIG. 21 is a diagram illustrating a diagnosis result of charge / discharge conditions (1C, 3C).

  Hereinafter, embodiments of the diagnosis apparatus and method will be described in detail with reference to the accompanying drawings.

(First embodiment)
FIG. 1 is a block diagram illustrating a hardware configuration of the diagnostic apparatus according to the first embodiment. As shown in FIG. 1, the determination of the residual value of the storage battery 2 is performed by an inspection device 3 that inspects the storage battery 2 and a diagnostic device 1 that actually diagnoses the residual value of the storage battery 2 based on the detection result of the inspection device 3. And a computer system having In this embodiment, the diagnostic device 1 which is one of the components of the computer system is a device group in which devices are combined via a communication network (not shown) such as a LAN (Local Area Network) or an intranet according to processing functions. Can be configured.

  The diagnostic apparatus 1 includes a CPU 100 (Central Processing Unit), a RAM (RWM) 110 (Random Access Memory (Read Write Memory)), a communication I / F 120, an input I / F 130 (Inter Face), and an output I / F 140. And a ROM 150 (Read Only Memory), a storage unit 160, and a timer 170. In addition, an IF (interface) for mounting an external storage device such as a USB memory may be provided. The diagnostic device 1 is a computer that executes and calculates a program.

  The diagnostic device 1 collects inspection data of the storage battery 2 such as a current value and a voltage value from the inspection device 3 via the communication I / F 120 and performs various arithmetic processes using the collected inspection data.

  The CPU 100 is an arithmetic processing unit (microprocessor) that reads each program written in advance in the ROM 150 into the RAM 110 and performs calculation processing. The CPU 100 can be configured by a plurality of CPU groups (microcomputers, microcontrollers) in accordance with functions. Further, the CPU 100 may include a built-in memory having a RAM function.

  The RAM 110 is a storage area used when the CPU 100 executes a program, and is a memory used as a working area. The RAM 110 is suitable for temporarily storing data necessary for processing.

  The communication I / F 120 is a communication device and a communication unit that exchange data with the inspection device 3. For example, there is a router. In the present embodiment, the connection between the communication I / F 120 and the inspection device 3 is described as in wired communication, but can be replaced with various wireless communication networks. Further, the connection between the communication I / F 120 and the inspection apparatus 3 may be performed via a network having a number of one-way or two-way communication.

  The input I / F 130 is an interface that connects the input unit 131 and the diagnostic apparatus 1. You may have the input control function which converts the input signal sent from the input part 131, and converts it into the signal which CPU100 can recognize. The input I / F 130 is not an essential component as a terminal or the like, and may be directly connected to the wiring in the diagnostic apparatus 1.

  The input unit 131 is an input device or an input unit that performs input control of various keyboards and buttons that are generally provided in a computer device. In addition, a function of recognizing or detecting an input signal by recognizing a voice uttered by a person may be provided. In the present embodiment, it is installed outside the diagnostic apparatus 1, but may be incorporated in the diagnostic apparatus 1.

  The output I / F 140 is an interface that connects the display unit 141 and the diagnostic apparatus 1. The display control of the display unit 141 may be performed from the CPU 100 via the output I / F 140, or the display control is performed by an LSI (Lage Scale Integrated Circuit) (GPU: Graphics Processing Unit) that performs drawing processing such as a graphic board. May be. As a display control function, for example, there is a decoding function for decoding image data. The diagnosis apparatus 1 may be configured such that the display unit 141 is directly connected to the inside of the diagnosis apparatus 1 without using the output I / F 140.

  The display unit 141 is an output device such as a liquid crystal display, an organic EL (Electro Luminescence) display, or a plasma display, and an output unit. In addition, you may provide the function to emit a sound. In the present embodiment, the display unit 141 is installed outside the diagnostic apparatus 1, but the display unit 141 may be incorporated inside the diagnostic apparatus 1.

  The ROM 150 is a memory that stores various programs. A non-primary storage medium that cannot write data is preferably used, but a storage medium such as a semiconductor memory that can read and write data at any time may be used. In addition, a display program for displaying image data with characters and symbols that can be recognized by a person on the display unit 141, a program for distributing contents such as deterioration information of the storage battery 2 to an external terminal via the communication I / F 120, and acquisition The information registration program etc. which memorize | store the performed data in the memory | storage part 160 for every predetermined time may be stored.

  The storage unit 160 is a non-volatile storage device such as a hard disk drive (HDD) or storage means. In addition, a semiconductor memory such as a flash memory may be used without being limited to the nonvolatile storage unit, or a storage medium in which these semiconductor memory and HDD are combined may be used. In the present embodiment, the ROM 150 and the storage unit 160 are described as different storage media, but the ROM 150 and the storage unit 160 may be combined into one storage unit and storage unit. The storage unit 160 stores various data necessary for the calculation process of the CPU 100, such as application programs executed by the CPU 100 when diagnosing the storage battery 2, the deterioration test data 40, the deterioration characteristic DB 22, and the like (see FIG. 2). Yes.

  The timer 170 is a clock for measuring time. Using the time measured by the timer 170, the CPU 100 measures and stores the current value and voltage value of the storage battery 2. The diagnostic device 1 may be configured to acquire time via a communication network instead of the timer 170, or to calculate the current time from the measurement by the timer 170 and the time obtained from the communication network. Also good.

  The inspection device 3 receives a command from the diagnostic device 1 and performs a characteristic test for detecting a plurality of current internal state quantities of the storage battery 2 to be diagnosed, that is, charge / discharge, resistance measurement, and capacity measurement of the storage battery 2 to be diagnosed. The temperature is measured, and the inspection data is sent to the diagnostic apparatus 1.

  FIG. 2 is a block diagram illustrating a functional configuration of the diagnostic apparatus 1 according to the first embodiment. In the diagnostic apparatus 1, the CPU 100 develops the application program stored in the storage unit 160 in the RAM 110 and sequentially executes the application program as the residual value diagnostic unit 10, the deterioration characteristic calculation / management unit 20, and the state quantity detection unit 30. Realize the function.

  The state quantity detection unit 30 detects the internal state quantity of the storage battery 2 using the inspection data of the storage battery 2 received from the inspection device 3 (details will be described later), and uses the detection result as the deterioration characteristic calculation / management unit 20. Input to the residual value diagnosis unit 10.

  The degradation characteristic calculation / management unit 20 receives a command from the diagnosis control unit 11 by the degradation characteristic management unit 23, and the degradation characteristic calculation unit 21 obtains test data measured for different test conditions from the degradation test data 40. This test data shows the deterioration of the internal state quantity of the storage battery with respect to the number of charge / discharge cycles under different test conditions (for example, charge / discharge conditions such as 1C, 2C, and 3C, temperature conditions such as 25 ° C, 35 ° C, and 45 ° C). This is data obtained from a plurality of test samples (a plurality of storage batteries). The deterioration test data 40 is storage means for storing deterioration characteristics obtained from a plurality of test samples for different test conditions.

  The deterioration characteristic calculator 21 gives the obtained test data to the state quantity detector 30 to detect a plurality of internal state quantities. The calculation results of the plurality of internal state quantities in the state quantity detection unit 30 are input to the deterioration characteristic calculation unit 21. The deterioration characteristic calculation unit 21 arranges the input calculation result for each test condition in association with the number of charge / discharge cycles obtained from the deterioration test data 40 and stores the result in the deterioration characteristic DB 22.

  The diagnosis control unit 11 inquires of the deterioration characteristic management unit 23 when the inspection device 3 detects a plurality of internal state quantities of the storage battery 2 to be diagnosed. In response to an inquiry from the diagnosis control unit 11, the deterioration characteristic management unit 23 corresponds to the number of charge / discharge cycles from the deterioration characteristic DB 22 for each of the stored internal state quantities for each test condition performed on a plurality of test samples. To obtain the equivalent history / likelihood calculation unit 12.

  The residual value diagnosis unit 10 includes a diagnosis control unit 11, an equivalent history / likelihood calculation unit 12, an existence probability distribution calculation unit 13, a residual value calculation unit 14, and a diagnosis result display unit 15. The diagnosis control unit 11 advances the diagnosis process of the residual value of the storage battery 2 in an integrated manner. The equivalent history / likelihood calculation unit 12 corresponds to a history of actually charging / discharging the storage battery with reference to test data based on the internal state quantity detected when the storage battery 2 is charged / discharged. The equivalent history is identified, and the likelihood (identification likelihood) of the equivalent history identified from the data for a plurality of test samples included in the test data is calculated. The existence probability distribution calculation unit 13 calculates the existence probability distribution of the identified equivalent history based on the identification likelihood calculated by the equivalent history / likelihood calculation unit 12. The residual value calculation unit 14 compares the existence probability distribution calculated by the existence probability distribution calculation unit 13 with limit values (usage limit levels) set in advance in various data in the storage unit 160 as the use limit of the storage battery 2. Based on the result, the residual value of the storage battery 2 is calculated. The diagnosis result display unit 15 displays the calculation result (diagnosis result) by the equivalent history / likelihood calculation unit 12, the existence probability distribution calculation unit 13, and the residual value calculation unit 14 on the display screen of the display unit 141.

  Specifically, the diagnosis control unit 11 starts diagnosing the residual value of the storage battery 2 after the storage battery 2 is attached to the inspection device 3. The diagnosis control unit 11 sends a command to the inspection device 3 to perform a characteristic inspection of the storage battery 2. Subsequently, when the detection of the internal state quantity in the state quantity detection unit 30 is completed, the diagnosis control unit 11 notifies the equivalent history / likelihood calculation unit 12 of the internal state quantity. Also, the degradation characteristic management unit 23 of the degradation characteristic calculation / management unit 20 is commanded, and the internal history data of each test condition associated with the number of charge / discharge cycles is obtained from the degradation characteristic DB 22 as an equivalent history / likelihood calculation unit. 12 to send.

  The equivalent history / likelihood calculation unit 12 obtains information on the internal state quantity from the state quantity detection unit 30, and at the same time, obtains internal state quantity data for each test condition associated with the number of charge / discharge cycles from the deterioration characteristic DB 22. Obtain. The equivalent history / likelihood calculation unit 12 inquires the internal state quantity of the storage battery 2 with the test data of the internal state quantity of each test condition, thereby obtaining the temperature condition (equivalent temperature condition) of the storage battery 2 as the equivalent history of the storage battery 2. ), The number of equivalent cycles corresponding to the number of cycles actually charged / discharged under the charge / discharge conditions (equivalent charge / discharge conditions) actually charged / discharged is identified. Next, the equivalent history / likelihood calculation unit 12 calculates the identification likelihood of the equivalent history identified from the data for a plurality of test samples included in the test data as a probability distribution.

  The existence probability distribution calculation unit 13 deteriorates the deterioration characteristics of a plurality of internal state quantities corresponding to the number of equivalent cycles under the equivalent temperature condition and the equivalent charge / discharge condition, which is the equivalent history identified by the equivalent history / likelihood calculation unit 12. An inquiry is made to the characteristic management unit 23. The deterioration characteristic management unit 23 has a plurality of internal state quantities corresponding to the number of equivalent cycles in the equivalent temperature condition and the equivalent charge / discharge condition, which is the equivalent history identified in the equivalent history / likelihood calculation unit 12 from the deterioration characteristic DB 22. The deterioration characteristic is obtained from the deterioration characteristic DB 22 and sent to the existence probability distribution calculation unit 13.

  The existence probability distribution calculation unit 13 stores a plurality of internal state quantities associated with the equivalent temperature conditions and the number of equivalent cycles under the equivalent charge / discharge conditions, which are equivalent histories identified by the equivalent history / likelihood calculation unit 12. 2 is converted into a representative index value such as a capacity of 2 and an internal resistance value, and an index value in the number of equivalent cycles identified by the equivalent history / likelihood calculation unit 12 is calculated. Further, the existence probability distribution in the calculated index value is obtained based on the identification likelihood calculated in the equivalent history / likelihood calculation unit 12.

  The residual value calculation unit 14 obtains a usage limit level in accordance with the intended use of the storage battery 2 from the diagnosis control unit 11 and uses the index value in the equivalent cycle number calculated by the existence probability distribution calculation unit 13 and the existence probability distribution. Then, the current residual value of the storage battery 2 is calculated by comparing with the use limit level. More specifically, the ratio at which the existence probability distribution exceeds the use limit level and the distance between the index value and the use limit level in the number of equivalent cycles are variables that determine the residual value.

  The diagnosis result display unit 15 displays the current residual value of the storage battery 2 calculated by the residual value calculation unit 14 such as the relationship between the existence probability distribution and the use limit level, and the ratio of the existence probability distribution exceeding the use limit level. A numerical value is displayed on the display screen of the display unit 141. The above-described operation sequence (residual value diagnosis process) is controlled by the diagnosis control unit 11 of the residual value diagnosis unit 10.

  FIG. 3 is a flowchart illustrating an example of the residual value diagnosis process. As illustrated in FIG. 3, the diagnosis control unit 11 confirms that the storage battery 2 to be diagnosed is attached to the inspection device 3 and starts a residual value diagnosis process (S101).

  Next, the diagnosis control unit 11 controls the inspection device 3 to acquire a charge / discharge curve of the storage battery 2 under a predetermined charging condition, and the inspection device 3 uses a plurality of storage batteries 2 based on the acquired charge / discharge curve. An internal state quantity is detected (S102). This internal state quantity can be detected using a known technique as described in Japanese Patent No. 3669673, for example. More specifically, using the discharge curve illustrated in FIG. 4, the parameters are estimated by a model expressing the discharge curve with the parameters and physical constants illustrated in FIG. 5 prepared in advance as various data. Can do. Further, as shown in FIG. 6, a plurality of internal state quantities such as the capacity of the active material A, the capacity of the active material B, and the resistance value of the electrode can be estimated from the charge curve by the same model as the internal state quantity. .

  On the other hand, the test data of the degradation test data 40 is processed in advance using the state quantity detection unit 30, and the degradation characteristic calculation / management unit 20 degrades a plurality of internal state quantities for the charge / discharge cycle for each temperature condition and charge / discharge condition. The characteristic is stored in the deterioration characteristic DB 22. Subsequent to S102, the equivalent history / likelihood calculation unit 12 compares the plurality of internal state quantities of the detected storage battery 2 using the deterioration characteristic DB 22, identifies the equivalent history having the highest likelihood, and the identification. The identification likelihood of the equivalent history is calculated (S103).

  FIG. 7 is a diagram illustrating an example of test data. The deterioration characteristics of a plurality of internal state quantities with respect to the charge / discharge cycle for each temperature condition and charge / discharge condition stored in the deterioration characteristic DB 22 are managed in a form as shown in FIG. More specifically, based on test data (circles in the figure) of a plurality of test samples (storage batteries), a plurality of calculated internal state quantities are measured at discrete charge / discharge cycle number intervals. Yes. FIG. 7 shows test data under a temperature condition of 35 ° C. and a charge / discharge condition of 1C (one charge / discharge cycle in one hour). In the test data of a plurality of test samples, there are variations in the measurement results at the discrete number of charge / discharge cycles where the measurement was performed. The solid line in the figure is an approximate curve connecting the median values, and the dotted line is a curve obtained by linear interpolation of points with standard deviation ± σ.

  FIG. 8 is a diagram illustrating identification of equivalent history. For the deterioration characteristics of the internal state quantity as shown in FIG. 7, the identification of the equivalent history is performed as shown in FIG. Specifically, the number of charge / discharge cycles corresponding to the detected value is determined for each internal state quantity (capacity of active material A, capacity of active material B, resistance value, etc.).

  Here, a case where the detected internal state quantities are three of s1, s2, and s3 is illustrated. With respect to the deterioration characteristic of the internal state quantity as shown in FIG. 7, the equivalent history is identified as shown in FIG. Specifically, the number of charge / discharge cycles corresponding to the detected value is obtained for each internal state quantity. For example, assuming that the number of charge / discharge cycles corresponding to the values detected as the capacity (s1) of the active material A, the capacity (s2) of the active material B, and the resistance value (s3) are N1, N2, and N3, respectively, the temperature condition Tp The identification error Eeq when charging / discharging condition Ccrg is obtained is obtained by the following equation (1).

Note that N1, N2, and N3 are obtained by the following equations, with the respective internal state quantities being equivalent cycle numbers corresponding to the center line of the deterioration characteristics of FIG.
N1 = Ncyc (Tp, s1)
N2 = Ncyc (Tp, s2)
N3 = Ncyc (Tp, s3)

  Also, the interval between the internal state quantities s1, s2, and s3 in relation to the curve obtained by linear interpolation of the points of standard deviation ± σ with respect to the approximate curve in which the median is connected as shown in FIG. Σ1, σ2, and σ3 are determined so that becomes 2σ.

  The equivalent cycle number Neq is determined by the following equation (2) as a value that minimizes the identification error Eeq.

  The equivalent cycle number Neq is determined for each temperature condition (Tp) and charge / discharge condition (Ccrg) so as to satisfy the above-described equations (1) and (2). When the temperature conditions stored in the deterioration characteristic DB 22 are three types of 25 ° C., 35 ° C., and 45 ° C. and the charge / discharge conditions are two types of 1C and 3C, a total of six equivalent cycle numbers Neq and identification error Eeq are obtained respectively. Of these, the equivalent number of cycles Neq when the identification error Eeq is the smallest is adopted as the identification result. Then, the temperature condition and charge / discharge condition of the identified equivalent cycle number Neq are determined (identified) as the equivalent temperature condition and equivalent charge / discharge condition. FIG. 8 shows that 35 ° C. is identified as the equivalent temperature condition and 1C charge / discharge is identified as the equivalent charge / discharge condition.

  FIG. 9 is a diagram illustrating the calculation of the identification likelihood of the equivalent history. As shown in FIG. 9, the identification likelihood is calculated when the identification likelihood in the identified equivalent history (number of equivalent cycles, equivalent temperature condition and equivalent charge / discharge condition) is present on the equivalent cycle as a normal distribution. Then, a normal distribution including all distributions is obtained as an identification likelihood distribution. Here, σA is the standard deviation for the active material A, σB is the standard deviation for the active material B, and σR is the standard deviation for the resistance value.

  As shown in FIG. 9, the identification likelihood is a normal distribution having σ1, σ2, and σ3 as standard deviations as identification likelihoods of the respective internal state quantities in the obtained equivalent temperature condition and equivalent charge / discharge condition. All distributions when N1, N2, and N3 exist in the center, that is, when distributions exist as normal distributions with σ1 ^ 2, σ2 ^ 2, and σ3 ^ 2 around N1, N2, and N3 Is obtained as an identification likelihood distribution. Let the standard deviation of this identification likelihood distribution be σeq.

  FIG. 10 is a flowchart illustrating an example of identification of the number of equivalent cycles and calculation of identification likelihood. As shown in FIG. 10, when the identification of the number of equivalent cycles and the calculation of the identification likelihood are started in S201, the diagnosis control unit 11 selects one charging rate Ccrg in the deterioration characteristic DB 22 (S202). . Next, the diagnosis control unit 11 sets the number of detected internal state quantities to m (S203), and selects one temperature condition among the p kinds of temperature conditions registered in the deterioration characteristic DB 22 (S204).

  Next, the equivalent history / likelihood calculation unit 12 obtains the number of equivalent cycles (Ncyc (Tp, sm)) of m internal state quantities under the selected temperature condition Tp, and calculates the relative error Eeq (Tp, Ccrg). Obtain (S205).

  Next, the diagnosis control unit 11 determines whether or not Eeq has been obtained for all p temperature conditions (S206). If not obtained (S206: NO), the process returns to S204. For example, when temperature conditions of 25 ° C., 35 ° C., and 45 ° C. are registered in the deterioration characteristic DB 22, the relative error Eeq (Tp, Ccrg) is obtained for the three types of temperature conditions. When Eeq is obtained for all p temperature conditions (S206: YES), the equivalent history / likelihood calculation unit 12 sets the temperature condition that minimizes the relative error Eeq (Tp, Ccrg) as the equivalent temperature condition, The equivalent cycle number Neq is selected (identified) (S207).

  Next, the equivalent history / likelihood calculation unit 12 calculates the identification likelihood as a probability density from the relative error of the equivalent cycle number Neq and Ncyc (Tp, sm) (S208), and the equivalent cycle number, equivalent temperature, and identification The standard deviation σeq of likelihood is specified (S209). By specifying the equivalent cycle number, the equivalent temperature, and the standard deviation σeq of the identification likelihood, the diagnosis control unit 11 ends the identification of the equivalent cycle number and the calculation of the identification likelihood (S210).

  Returning to FIG. 3, following S103, the existence probability distribution calculation unit 13 calculates the existence probability distribution from the equivalent history and the identification likelihood using the deterioration characteristic DB 22 (S104). Specifically, among the identified equivalent histories, data of a plurality of internal state quantities under the equivalent temperature condition and equivalent charge / discharge condition are read from the deterioration characteristic DB 22 and converted into a representative index value of the storage battery 2. Thus, an index value in the number of equivalent cycles is calculated. Further, an existence probability distribution in the index value is obtained according to the identification likelihood.

  FIG. 11 is a conceptual diagram illustrating the calculation and display of the existence probability distribution of the capacity of the storage battery 2. FIG. 12 is a conceptual diagram illustrating the calculation and display of the existence probability distribution of the resistance value of the storage battery 2. As shown in FIGS. 11 and 12, the existence probability distribution G1 for the capacity of the storage battery 2 subjected to the function conversion and the existence probability distribution G2 for the resistance value of the storage battery 2 are obtained based on the identification likelihood of the number of equivalent cycles. The capacity of the storage battery 2 can be expressed as the sum of the capacities of the active materials A and B when the active material A and the active material B are the capacities of three main active materials of the electrode of the storage battery 2.

  For example, when the equivalent cycle number is obtained as a value at an equivalent temperature condition of 35 ° C., the deterioration characteristic of the index value at 35 ° C. is derived from the deterioration characteristic DB 22, and the representative index value at the equivalent cycle number Neq, that is, the capacity And a normal distribution having a capacity and an internal resistance value of 1 σ at the number of cycles of Neq ± σeq, respectively, as the existence probability distribution.

  Next, the residual value calculation unit 14 compares the index value calculated by the existence probability distribution calculation unit 13 and the existence probability distribution at this index value with the use limit level, and the ratio (existence) where the existence probability distribution exceeds the use limit level. The residual value of the storage battery 2 is calculated from the ratio of the area across the use limit level to the total area of the probability distributions G1 and G2) or the distance between the use limit level and the median value (S105). Next, the diagnosis result display unit 15 displays the residual value calculated by the residual value calculation unit 14 on the display screen G of the display unit 141 (S106), and the diagnosis control unit 11 ends the residual value determination (S107).

  Specifically, as shown in FIGS. 11 and 12, a graph of the capacity and resistance value existence probability distributions G1 and G2 of the storage battery 2 and the use limit level is displayed on the display screen G so that the comparison is possible. The user can easily grasp the remaining value of the storage battery 2 by checking the display screen G described above.

  13, 14, 15, and 16 are conceptual diagrams illustrating the output of the residual value diagnosis result. 11 and 12, the capacity and resistance value existence probability distributions G <b> 1 and G <b> 2 of the storage battery 2 and the usage limit level are displayed on the display screen G for visual comparison, but FIGS. As shown, the quantitative value actually calculated may be displayed on the display screen G.

  Further, when the storage battery 2 to be diagnosed is composed of a plurality of (q) power storage cells, the representative equivalent charge / discharge conditions and the nonconformity rate at the representative equivalent temperature are displayed on the display screen G as shown in FIG. Also good.

  Further, as shown in FIG. 16, without obtaining the existence probability distribution, the index value G4 at the equivalent cycle number Neq + σeq is displayed on the display screen G as the remaining value of the current storage battery 2 under the worst condition, The residual value may be evaluated by distance.

  FIG. 17 is a conceptual diagram illustrating the display of the existence probability distribution of the capacity and resistance value of the storage battery 2. When there are two index values as index values calculated by the existence probability distribution calculation unit 13, and the existence probability distribution at this index value is calculated, as shown in FIG. The existence probability distribution may be displayed and output on a plane having the two index values as two axes. Specifically, based on the capacity and resistance value existence probability distributions G1 and G2 of the storage battery 2, a plan view in the form of connecting the same existence probability density G3 with a line (in the illustrated example, an ellipse) is displayed on the display screen G. May be displayed. In this case, the residual value of the storage battery 2 can be easily grasped from the existence probability density G3 in the capacity and resistance value of the storage battery 2.

(Second Embodiment)
FIG. 18 is a block diagram illustrating a functional configuration of the diagnostic apparatus 1 according to the second embodiment. As shown in FIG. 18, the second embodiment is different from the first embodiment in that the diagnostic result DB 5 stored in the storage unit 160 or the like is provided. The diagnosis result DB 5 is a recording unit that records the internal state quantity of the storage battery 2 detected by the state quantity detection unit 30 in association with the equivalent history identified by the equivalent history / likelihood calculation unit 12.

  FIG. 19 is a flowchart illustrating an example of the residual value diagnosis process. More specifically, FIG. 19 shows, in addition to the residual value diagnosis process (S110) illustrated in FIG. 3, the internal state quantity of the storage battery 2 detected by the state quantity detection unit 30 is converted into an equivalent history / likelihood calculation unit. 12 is a flowchart including a step of recording in association with the equivalent history identified in 12. As shown in FIG. 19, after performing the residual value diagnosis process (S <b> 110) illustrated in FIG. 3, the deterioration characteristic calculation / management unit 20 is detected (estimated) by the state quantity detection unit 30 according to a command from the diagnosis control unit 11. The internal state quantity of the storage battery 2 is recorded in the diagnosis result DB 5 in association with the equivalent history (for example, the number of equivalent cycles) identified by the equivalent history / likelihood calculation unit 12 (S111).

  The equivalent history / likelihood calculation unit 12 obtains information on the internal state quantity from the state quantity detection unit 30, and at the same time, the internal state quantity data of each test condition associated with the number of charge / discharge cycles from the deterioration characteristic DB 22. By referring to the data recorded in the diagnosis result DB 5, as the equivalent history of the storage battery 2, the temperature condition (equivalent temperature condition) of the storage battery 2 and the charge / discharge condition (equivalent charge / discharge condition) actually charged / discharged Then, the number of equivalent cycles corresponding to the number of cycles actually charged / discharged is identified. Next, the equivalent history / likelihood calculation unit 12 adds the data recorded in the diagnosis result DB 5 to the data for a plurality of test samples included in the test data, and calculates the identification likelihood of the identified equivalent history as a probability distribution. To do. That is, in the second embodiment, the diagnosis result obtained in S110 is reused as one of new test data.

  FIG. 20 is a conceptual diagram illustrating the reuse of diagnosis results. As shown in FIG. 20, in the second embodiment, in addition to the data for the test sample included in the deterioration test data 40 (circles other than the equivalent cycle number Neq), it corresponds to the equivalent cycle number Neq identified in S111. The data (circles in the inspection results) can be reused as one of the new test data. For this reason, every time the residual value of the storage battery 2 is diagnosed, data as a test sample can be enriched.

(Modification)
In the first and second embodiments described above, the equivalent history is obtained according to one charge / discharge condition. In the modified example, the equivalent history is obtained for each of the plurality of charge / discharge conditions, and the results are combined to obtain the residual value of the storage battery 2. Specifically, the number of equivalent cycles under the charge / discharge condition of 1C and the number of equivalent cycles under the charge / discharge condition of 3C are obtained, and the respective likelihood of identification is calculated. Next, the identification likelihoods are compared, and the diagnosis result with the smaller standard deviation of the identification likelihood is adopted.

  Specifically, as shown in FIG. 21, a diagnosis is performed on the number of equivalent cycles under 1C charge / discharge conditions and the number of equivalent cycles under 3C charge / discharge conditions. In the example of FIG. 21, the charge / discharge condition of 3C has a higher capacity nonconformity (%). Here, when the standard deviation of the identification likelihood is smaller under the charge / discharge condition of 1C, the nonconformity (%) of 1C is adopted as the residual value with higher accuracy. For this reason, the residual value can be determined more accurately.

  Further, the calculated residual value may be notified to a microcontroller or the like that controls charging / discharging of the storage battery 2. In this case, for example, when controlling charging / discharging of a plurality of storage batteries 2 with a microcontroller or the like, the charging / discharging is performed so that the storage battery 2 having a high residual value (low degradation) is used for charging / discharging with reference to the notification. By determining the storage battery 2 to be discharged, the residual values of the plurality of storage batteries 2 can be made uniform.

  In addition, the internal state quantity of the storage battery 2 described above may be a capacity or an internal resistance value that is a typical index value. In addition, when the identification likelihood for each internal state quantity is too large and there are not a plurality of reliable internal state quantities, the remaining value of the storage battery 2 may be diagnosed using the single internal state quantity. At this time, the identification likelihood of the number of equivalent cycles does not have a probability distribution and is uniquely obtained from the index value, so the residual value is determined by the distance between the obtained index value and the use limit level.

  Further, the number of charge / discharge cycles associated with the internal state quantity of the storage battery 2 and the deterioration characteristic of the index value may be a cumulative heat time obtained by integrating the temperature condition experienced by the storage battery 2 on a trial basis, or simply It may be time.

  Note that the program executed by the diagnostic apparatus 1 of the present embodiment is provided by being incorporated in advance in a ROM or the like. The program executed by the diagnosis apparatus 1 of the present embodiment is a file in an installable format or an executable format, and is a computer such as a CD-ROM, a flexible disk (FD), a CD-R, a DVD (Digital Versatile Disk). You may comprise so that it may record and provide on a readable recording medium.

  Furthermore, the program executed by the diagnostic apparatus 1 of the present embodiment may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network. Further, the program executed by the diagnostic device 1 of the present embodiment may be configured to be provided or distributed via a network such as the Internet.

  The program executed by the diagnostic apparatus 1 according to the present embodiment has a module configuration including the above-described functional configuration. As actual hardware, a CPU (processor) reads the program from the ROM and executes the program. The functional configuration is loaded onto the main storage device and generated on the main storage device.

  Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. Moreover, various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, the constituent elements over different embodiments may be appropriately combined.

  DESCRIPTION OF SYMBOLS 1 ... Diagnosis apparatus, 2 ... Storage battery, 3 ... Inspection apparatus, 5 ... Diagnosis result DB, 10 ... Residual value diagnosis part, 11 ... Diagnosis control part, 12 ... Equivalent history and likelihood calculation part, 13 ... Existence probability distribution calculation part , 14 ... residual value calculation unit, 15 ... diagnosis result display unit, 20 ... deterioration characteristic calculation / management unit, 21 ... deterioration characteristic calculation unit, 22 ... deterioration characteristic DB, 23 ... deterioration characteristic management unit, 30 ... state quantity detection unit 40 ... Degradation test data, 100 ... CPU, 110 ... RAM, 120 ... Communication I / F, 130 ... Input I / F, 131 ... Input unit, 140 ... Output I / F, 141 ... Display unit, 150 ... ROM, 160 ... storage unit, 170 ... timer, G ... display screen, G1, G2 ... existence probability distribution, G3 ... existence probability density, Neq ... equivalent cycle number

Claims (9)

Storage means for storing test data obtained from a plurality of test samples for the deterioration characteristics of the internal state quantity of the storage battery with respect to the number of charge / discharge cycles for each different test condition;
Based on the internal state quantity of the storage battery detected when charging / discharging the storage battery to be diagnosed, an equivalent history corresponding to the history of actually charging / discharging the storage battery with reference to the test data is obtained. First calculating means for identifying and calculating the likelihood of the identified equivalent history from data for a plurality of test samples included in the test data;
Output means for outputting the identified equivalent history and the calculated likelihood;
A diagnostic device comprising: The equivalent history is at least one of the number of equivalent cycles corresponding to the number of cycles actually charged / discharged, the temperature condition of the storage battery, and the charge / discharge conditions corresponding to the charge / discharge conditions actually charged / discharged. ,
The diagnostic device according to claim 1. The first calculation means is an equivalent that minimizes an error between each of the equivalent histories based on the detected plurality of internal state quantities and a center value for a plurality of test samples included in the test data. Identify in history,
The diagnostic device according to claim 1 or 2. Based on the calculated likelihood, further comprising second calculating means for calculating an existence probability distribution of the identified equivalent history;
The output means outputs the calculated existence probability distribution;
The diagnostic apparatus as described in any one of Claims 1 thru | or 3. The second calculating means calculates an existence probability distribution of the internal state quantity of the storage battery based on the calculated existence probability distribution;
The output means outputs an existence probability distribution of the calculated internal state quantity;
The diagnostic device according to claim 4. The second calculation means calculates an existence probability distribution of two internal state quantities of the storage battery based on the calculated existence probability distribution,
The output means displays and outputs the calculated existence probability distribution of the two internal state quantities on a plane having the two internal state quantities as two axes.
The diagnostic device according to claim 5. Based on a comparison result between the calculated existence probability distribution and a limit value set in advance as a use limit of the storage battery, further comprising third calculation means for calculating a residual value of the storage battery;
The output means outputs the calculated residual value;
The diagnostic device according to any one of claims 1 to 6. A recording means for recording diagnostic result data in which the detected internal state quantity is associated with the identified equivalent history;
The first calculation means identifies the equivalent history with reference to the recorded diagnosis result data together with the test data, data for a plurality of test samples included in the test data, and the recorded diagnosis Based on the result data, the likelihood of the identified equivalent history is calculated.
The diagnostic device according to any one of claims 1 to 7. A method of a diagnostic device comprising a storage means for storing test data obtained from a plurality of test samples for the deterioration characteristics of the internal state quantity of the storage battery with respect to the number of charge / discharge cycles for different test conditions,
Based on the internal state quantity of the storage battery detected when charging / discharging the storage battery to be diagnosed, an equivalent history corresponding to the history of actually charging / discharging the storage battery with reference to the test data is obtained. Identifying and calculating the likelihood of the identified equivalent history from the data for a plurality of test samples included in the test data;
Outputting the identified equivalent history and the calculated likelihood;
Including methods.

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